Search Results

You are looking at 11 - 18 of 18 items for

  • Author or Editor: Richard Bankert x
  • Refine by Access: All Content x
Clear All Modify Search
Richard L. Bankert
,
Cristian Mitrescu
,
Steven D. Miller
, and
Robert H. Wade

Abstract

Cloud-type classification based on multispectral satellite imagery data has been widely researched and demonstrated to be useful for distinguishing a variety of classes using a wide range of methods. The research described here is a comparison of the classifier output from two very different algorithms applied to Geostationary Operational Environmental Satellite (GOES) data over the course of one year. The first algorithm employs spectral channel thresholding and additional physically based tests. The second algorithm was developed through a supervised learning method with characteristic features of expertly labeled image samples used as training data for a 1-nearest-neighbor classification. The latter’s ability to identify classes is also based in physics, but those relationships are embedded implicitly within the algorithm. A pixel-to-pixel comparison analysis was done for hourly daytime scenes within a region in the northeastern Pacific Ocean. Considerable agreement was found in this analysis, with many of the mismatches or disagreements providing insight to the strengths and limitations of each classifier. Depending upon user needs, a rule-based or other postprocessing system that combines the output from the two algorithms could provide the most reliable cloud-type classification.

Full access
Richard L. Bankert
,
Jeremy E. Solbrig
,
Thomas F. Lee
, and
Steven D. Miller

Abstract

The Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) nighttime visible channel was designed to detect earth–atmosphere features under conditions of low illumination (e.g., near the solar terminator or via moonlight reflection). However, this sensor also detects visible light emissions from various terrestrial sources (both natural and anthropogenic), including lightning-illuminated thunderstorm tops. This research presents an automated technique for objectively identifying and enhancing the bright steaks associated with lightning flashes, even in the presence of lunar illumination, derived from OLS imagery. A line-directional filter is applied to the data in order to identify lightning strike features and an associated false color imagery product enhances this information while minimizing false alarms. Comparisons of this satellite product to U.S. National Lightning Detection Network (NLDN) data in one case as well as to a lightning mapping array (LMA) in another case demonstrate general consistency to within the expected limits of detection. This algorithm is potentially useful in either finding or confirming electrically active storms anywhere on the globe, particularly those occurring in remote areas where surface-based observations are not available. Additionally, the OLS nighttime visible sensor provides heritage data for examining the potential usefulness of the Visible-Infrared Imager-Radiometer Suite (VIIRS) Day/Night Band (DNB) on future satellites including the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP). The VIIRS DNB will offer several improvements to the legacy OLS nighttime visible channel, including full calibration and collocation with 21 narrowband spectral channels.

Full access
Michael F. Donovan
,
Earle R. Williams
,
Cathy Kessinger
,
Gary Blackburn
,
Paul H. Herzegh
,
Richard L. Bankert
,
Steve Miller
, and
Frederick R. Mosher

Abstract

Three algorithms based on geostationary visible and infrared (IR) observations are used to identify convective cells that do (or may) present a hazard to aviation over the oceans. The performance of these algorithms in detecting potentially hazardous cells is determined through verification with Tropical Rainfall Measuring Mission (TRMM) satellite observations of lightning and radar reflectivity, which provide internal information about the convective cells. The probability of detection of hazardous cells using the satellite algorithms can exceed 90% when lightning is used as a criterion for hazard, but the false-alarm ratio with all three algorithms is consistently large (∼40%), thereby exaggerating the presence of hazardous conditions. This shortcoming results in part from the algorithms’ dependence upon visible and IR observations, and can be traced to the widespread prevalence of deep cumulonimbi with weak updrafts but without lightning over tropical oceans, whose origin is attributed to significant entrainment during ascent.

Full access
Jeffrey D. Hawkins
,
Jeremy E. Solbrig
,
Steven D. Miller
,
Melinda Surratt
,
Thomas F. Lee
,
Richard L. Bankert
, and
Kim Richardson

Abstract

Global monitoring of tropical cyclones (TC) is enhanced by the unique capabilities provided by the day–night band (DNB), a sensor included on the Visible Infrared Imaging Radiometer Suite (VIIRS) flying on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The DNB, a low-light visible–near-infrared-band passive radiometer, can leverage unconventional (i.e., nonsolar) sources of visible light illumination such as moonlight to infer storm structure at night. The DNB provides an unprecedented capability to resolve moonlit clouds at high resolution, offering numerous potential benefits to both operational TC analysts and researchers developing new methods of monitoring TCs occurring within the largely data-void tropical oceanic basins. DNB digital data provide significant enhancements over older nighttime visible data from the Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) by leveraging accurate calibration, high sensitivity, and sub-kilometer-scale imagery that covers 2–3 times the moon’s lunar cycle than the OLS. By leveraging these attributes, DNB data can enable the use of automated objective applications instead of subjective image interpretation. Here, the authors detail ways in which critical information about TC structure, location, intensity changes, shear environment, lightning, and other characteristics can be extracted when the DNB data are used in isolation or in a multichannel approach with coincident infrared (IR) channels.

Open access
Arunas Kuciauskas
,
Jeremy Solbrig
,
Tom Lee
,
Jeff Hawkins
,
Steven Miller
,
Mindy Surratt
,
Kim Richardson
,
Richard Bankert
, and
John Kent
Full access
Sue Chen
,
Carolyn A. Reynolds
,
Jerome M. Schmidt
,
Philippe P. Papin
,
Matthew A. Janiga
,
Richard Bankert
, and
Andrew Huang

Abstract

A high-impact atmospheric river (AR) event that made landfall on the U.S. West Coast on Valentine’s Day of 2019 and produced widespread flooding in California is examined. The U.S. Naval Research Laboratory cloud resolving and high-resolution Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) captures the main features impacting the life cycle and structure of the Valentine’s Day AR. Analysis of the model-simulated AR reveals the complex processes leading up to the initial northeastward surge of the water vapor and enhanced near-surface flow associated with this AR. These include the preexistence of a mesoscale cold-core kona low, a mesoscale anticyclone, and a strong low-level convergence in the corridor between the kona low and mesoscale anticyclone where the environment becomes supersaturated in a region of weak vertical wind shear. Model sensitivity experiments show that the eastward progression and magnitude of the AR water vapor surge are strongly sensitive to the magnitude of kona low circulation. Experiments with the kona low circulation amplitude reduced to less than 25% showed that the AR is not able to reach the U.S. West Coast. These results help to identify key new aspects of an important player—the kona low—and its significant contributions to the overall AR characteristics of this particular observed event.

Open access
Steven D. Miller
,
John M. Forsythe
,
Philip T. Partain
,
John M. Haynes
,
Richard L. Bankert
,
Manajit Sengupta
,
Cristian Mitrescu
,
Jeffrey D. Hawkins
, and
Thomas H. Vonder Haar

Abstract

The launch of the NASA CloudSat in April 2006 enabled the first satellite-based global observation of vertically resolved cloud information. However, CloudSat’s nonscanning W-band (94 GHz) Cloud Profiling Radar (CPR) provides only a nadir cross section, or “curtain,” of the atmosphere along the satellite ground track, precluding a full three-dimensional (3D) characterization and thus limiting its utility for certain model verification and cloud-process studies. This paper details an algorithm for extending a limited set of vertically resolved cloud observations to form regional 3D cloud structure. Predicated on the assumption that clouds of the same type (e.g., cirrus, cumulus, and stratocumulus) often share geometric and microphysical properties as well, the algorithm identifies cloud-type-dependent correlations and uses them to estimate cloud-base height and liquid/ice water content vertical structure. These estimates, when combined with conventional retrievals of cloud-top height, result in a 3D structure for the topmost cloud layer. The technique was developed on multiyear CloudSat data and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) swath data from the NASA Aqua satellite. Data-exclusion experiments along the CloudSat ground track show improved predictive skill over both climatology and type-independent nearest-neighbor estimates. More important, the statistical methods, which employ a dynamic range-dependent weighting scheme, were also found to outperform type-dependent near-neighbor estimates. Application to the 3D cloud rendering of a tropical cyclone is demonstrated.

Full access
Theodore M. McHardy
,
James R. Campbell
,
David A. Peterson
,
Simone Lolli
,
Richard L. Bankert
,
Anne Garnier
,
Arunas P. Kuciauskas
,
Melinda L. Surratt
,
Jared W. Marquis
,
Steven D. Miller
,
Erica K. Dolinar
, and
Xiquan Dong

Abstract

We describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite 16 (GOES-16) and developed with collocated Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) channel-4 (1.378 μm) radiance and CALIOP 0.532-μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378-μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine-learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the channel-4 radiance as a function of AMF. The algorithm detects nearly 50% of subvisual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semiquantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378-μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an overland algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.

Full access